With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

To explore the value of general information and rapid laboratory tests obtained from the emergency department in the early diagnosis and prevention of young patients with acute myocardial infarction and acute myocarditis, in order to prevent the disease from progressing to a critical stage. This study employs a retrospective observational study, compiling clinical data from young patients diagnosed with acute myocardial infarction or acute myocarditis who were admitted to the Department of Cardiology or Emergency Department of the Second Affiliated Hospital of Soochow University from January 2015 to September 2024. Demographic information and laboratory test results from both the outpatient and emergency departments were retrieved. The acute myocardial infarction group comprised 267 patients (257 males, 10 females) aged 23-44 ys, while the acute myocarditis group included 134 patients (93 males, 41 females) aged 18-44 ys. A comparative analysis of the clinical data between the two groups was conducted, encompassing variables such as age, gender, comorbidities, high-risk factors, emergency blood routine tests, high-sensitivity C-reactive protein levels, coagulation profiles, renal function tests, NT-proBNP levels, myocardial injury markers, electrocardiogram readings, blood pressure, and heart rate. The results showed that:Compared with the young myocarditis group, the myocardial infarction group was older (ys)[38(35, 42) vs 30(25, 37), U=7 893, P<0.001], more male [257(96.3%) vs 93(69.4%), χ2=57.95, P<0.001], more smoking [211(79.0%) vs 38(28.4%), χ2=97.32, P<0.001], drinking history [125(46.8%) vs 22(16.4%), χ2=35.51, P<0.001], family history of coronary heart disease [45(16.9%) vs 3(2.2%), χ2=18.09, P<0.001], hypertension [100(37.5%) vs 12(9.0%), χ2=36, P<0.001] and diabetes [42(15.7%) vs 4(3.0%), χ2=14.27, P<0.001]. Systolic blood pressure (mmHg)[126(114, 144) vs 119(101, 126), U=11 389.50, P<0.001], diastolic blood pressure (mmHg)[80(70, 93) vs 72(62, 81), U=12 220.50, P<0.001], total white blood cell count (10 9/L)[11.3(9.2, 14.1) vs 8.5(6.6, 11.2), U=10 825.50, P<0.001], hemoglobin (g/L)[157(147, 166) vs 143(129, 154), U=9 404.50, P<0.001], platelet count (10 9/L)[244(206, 297) vs 207(173, 253), U=11 680, P<0.001], uric acid (μmol/L)[380(315, 446) vs 347(265, 412), U=14 805.50, P=0.005], ST segment elevation [204(76.4%) vs 57(42.5%), χ2=73.03, P<0.001] and Q wave formation [76(28.5%) vs 17(12.7%), χ2=12.47, P<0.001] in ECG were higher than those in myocarditis group. The duration of onset (hs) [6(3, 25) vs 48(24, 73), U=27911, P<0.001], heart rate (beats/min)[82(74, 92) vs 92(78, 103), U=22 347, P<0.001], D-dimer (μg/ml)[0.23(0.17, 0.51) vs 0.61(0.30, 1.38), U=25 806, P<0.001], High-sensitivity troponin T/99th percentile upper reference limit [5(1, 36) vs 16(8, 39), U=22 577, P<0.001], NT-proBNP (pg/ml) [204(64, 644) vs 824(189, 4 043), U=25 134, P<0.001], C-reactive protein (mg/L)[6(3, 9) vs 24(6, 55), U=26 349.50, P<0.001] and body temperature (℃) [36.50(36.30, 36.60) vs 37.35(36.50, 38.50), U=26 961, P<0.001] were significantly lower than those in myocarditis group, the symptoms of chest pain in myocardial infarction group was significantly higher than those in myocarditis group [262(98.1%) vs 83(61.9%), χ2=97.24, P<0.001], and the history of prodromal infection [12(4.5%) vs 112(83.6%), χ2=261.26, P<0.001], syncope [11(4.1%) vs 18(13.4%), χ2=11.53, P<0.001] and shock [6(2.2%) vs 22(16.4%), χ2=27.59, P<0.001] in myocardial infarction group were significantly lower than those in myocarditis group. With acute myocardial infarction as the target outcome, 8 influencing factors selected by LASSO regression, and 5 independent influencing factors were found after multiple Logistic regression, those were age ( OR=1.21, 95% CI: 1.12-1.31; P<0.001), pre-infection ( OR=0.02, 95% CI: 0.01-0.06; P<0.001), body temperature ( OR=0.37, 95% CI: 0.18-0.77; P=0.008), chest pain ( OR=26.75, 95% CI: 5.87-121.81; P<0.001) and white blood cell count ( OR=1.27, 95% CI: 1.12-1.44; P<0.001). Younger age, high body temperature and pre-infection are independent predictors for acute myocarditis, while chest pain and elevated white blood cell count are independent predictors for acute myocardial infarction. The five influencing factors selected by multivariate logistic regression and their combined diagnostic model were subjected to ROC analysis. The AUC reached 0.969, sensitivity reached 0.940 and specificity reached 0.925. Calibration curve and decision curve analysis(DCA) demonstrate that the model possesses excellent clinical application value. In conclusion, age, chest pain, pre-infection, body temperature and white blood cell count were independent factors in distinguishing acute myocardial infarction and acute myocarditis in young people. The clinical differential diagnosis model based on 5 independent factors may has high efficiency and good clinical practicability.

Objective:To analyze the associated factors and cumulative effects of cardiometabolic multimorbidity (CMM) among residents in southern Xinjiang.Methods:A stratified random cluster sampling method was used to conduct questionnaire surveys, physical examinations and laboratory tests among the personnel of the 51st Brigade, 3rd Division, Xinjiang, in 2016. The multivariate logistic regression, multivariate linear regression, restricted cubic spline, and network analysis methods were used to study the association of lifestyle (smoking, alcohol consumption and physical activity), socioeconomic (occupation, education and marital status) and clinical factors (waist circumference, body mass index and family history) with CMM.Results:A total of 12 773 study subjects were included. The prevalence of cardiovascular metabolic diseases among residents in southern Xinjiang was 52.49%. Specifically, the prevalence rates of dyslipidemia, hypertension, coronary heart disease, diabetes, and stroke were 31.14%, 29.95%, 6.78%, 6.26%, and 2.47%, respectively, and the prevalence of CMM was 19.06%. Multivariate logistic regression analysis revealed that the associations between clinical and socioeconomic factors and CMM significantly increased with higher scores. Specifically, the OR rose from 1.75 (clinical factors) and 1.07 (socioeconomic factors) on a score of 1 to 4.41 and 1.93 on a score of 3, respectively. The association between lifestyle factors and CMM was only observed at higher scores ( OR=1.26, 95% CI:1.07~1.62). The trend test using the scores of each group as continuous variables in the model showed that the risk of disease increased with the accumulation of clinical, socioeconomic and lifestyle factors (all P<0.05). Restricted cubic spline analysis demonstrated a non-linear relationship between the total number of associated factors and CMM ( Poverall<0.05 and Pnon-linear<0.05). Network analysis identified hypertension (strength=0.42) as the “core node” among the five diseases. When analyzing the three types of influencing factors, hypertension (strength=0.68), dyslipidemia (strength=0.47), coronary heart disease (strength=0.37), and clinical factors (strength=0.53) emerged as “core nodes”. In the network of nine associated factors, abnormal waist circumference and BMI (strength=0.90 and 0.84) were identified as “key factors”, while hypertension (strength=0.68) and dyslipidemia (strength=0.52) were identified as “key diseases”. Conclusion:The prevalence of CMM among residents in southern Xinjiang is high, and there is a cumulative effect of multiple factors. Hypertension and dyslipidemia are key diseases in the multimorbidity network, while abnormal BMI and waist circumference are key associated factors.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

To guide clinical blood transfusion practices for pediatric patients, the National Health Commission has issued the health standard "Guideline for pediatric transfusion" (WS/T 795-2022). Blood transfusion is one of the most commonly used supportive treatments for children with hematological diseases. This guideline provides guidance and recommendations for blood transfusions in children with aplastic anemia, thalassemia, autoimmune hemolytic anemia, glucose-6-phosphate dehydrogenase deficiency, acute leukemia, myelodysplastic syndromes, immune thrombocytopenic purpura, and thrombotic thrombocytopenic purpura. This article presents the evidence and interpretation of the blood transfusion provisions for children with hematological diseases in the "Guideline for pediatric transfusion", aiming to assist in the understanding and implementing the blood transfusion section of this guideline.
Humans ; Child ; Hematologic Diseases/therapy* ; Blood Transfusion/standards* ; Practice Guidelines as Topic

To guide clinical blood transfusion practices for pediatric patients, the National Health Commission has issued the health standard "Guideline for pediatric transfusion" (WS/T 795-2022). Blood transfusion for children undergoing hematopoietic stem cell transplantation is highly complex and challenging. This guideline provides recommendations on transfusion thresholds and the selection of blood components for these children. This article presents the evidence and interpretation of the transfusion provisions for children undergoing hematopoietic stem cell transplantation, with the aim of enhancing the understanding and implementation of the "Guideline for pediatric transfusion".
Humans ; Hematopoietic Stem Cell Transplantation ; Child ; Blood Transfusion/standards* ; Practice Guidelines as Topic

To guide clinical blood transfusion practices for pediatric patients, the National Health Commission has issued the health standard "Guideline for pediatric transfusion" (WS/T 795-2022). Critically ill children often present with anemia and have a higher demand for transfusions compared to other pediatric patients. This guideline provides guidance and recommendations for blood transfusions in cases of general critical illness, septic shock, acute brain injury, extracorporeal membrane oxygenation, non-life-threatening bleeding, and hemorrhagic shock. This article interprets the background and evidence of the blood transfusion provisions for critically ill and severely bleeding children in the "Guideline for pediatric transfusion", aiming to enhance understanding and implementation of this aspect of the guidelines. Citation:Chinese Journal of Contemporary Pediatrics, 2025, 27(4): 395-403.
Humans ; Critical Illness ; Blood Transfusion/standards* ; Child ; Hemorrhage/therapy* ; Practice Guidelines as Topic

To guide clinical blood transfusion practices in pediatric patients, the National Health Commission has issued the health standard "Guideline for pediatric transfusion" (WS/T 795-2022). Children undergoing cardiac surgery are at high risk of bleeding, and the causes of perioperative anemia and coagulation disorders in neonates and children are complex and varied, often necessitating the transfusion of allogeneic blood components. This guideline provides direction and recommendations for specific measures in blood management for children undergoing cardiac surgery before, during, and after surgery. This article interprets the background and evidence for the formulation of the blood transfusion provisions for children undergoing cardiac surgery, hoping to facilitate the understanding and implementation of this guideline.
Humans ; Cardiac Surgical Procedures ; Blood Transfusion/standards* ; Child ; Practice Guidelines as Topic

OBJECTIVE: To investigate the characteristics of gut microbiota changes in patients with acute myeloid leukemia (AML) undergoing induction chemotherapy and to explore the relationship between infectious complications and gut microbiota. METHODS: Fecal samples were collected from 37 newly diagnosed AML patients at four time points: before induction chemotherapy, during chemotherapy, during the neutropenic phase, and during the recovery phase. Metagenomic sequencing was used to analyze the dynamic changes in gut microbiota. Correlation analyses were conducted to assess the relationship between changes in gut microbiota and the occurrence of infectious complications. RESULTS: During chemotherapy, the gut microbiota α-diversity (Shannon index) of AML patients exhibited significant fluctuations. Specifically, the diversity decreased significantly during induction chemotherapy, further declined during the neutropenic phase (P < 0.05, compared to baseline), and gradually recovered during the recovery phase, though not fully returning to baseline levels.The abundances of beneficial bacteria, such as Firmicutes and Bacteroidetes, gradually decreased during chemotherapy, whereas the abundances of opportunistic pathogens, including Enterococcus, Klebsiella, and Escherichia coli, progressively increased.Analysis of the dynamic changes in gut microbiota of seven patients with bloodstream infections revealed that the bloodstream infection pathogens could be detected in the gut microbiota of the corresponding patients, with their abundance gradually increasing during the course of infection. This finding suggests that bloodstream infections may be associated with opportunistic pathogens originating from the gut microbiota.Compared to non-infected patients, the baseline samples of infected patients showed a significantly lower relative abundance of Bacteroidetes (P < 0.05). Regression analysis indicated that Bacteroidetes abundance is an independent predictive factor for infectious complications (P < 0.05, OR =13.143). CONCLUSION During induction chemotherapy in AML patients, gut microbiota α-diversity fluctuates significantly, and the abundance of opportunistic pathogens increase, which may be associated with bloodstream infections. Patients with lower baseline Bacteroidetes abundance are more prone to infections, and its abundance can serve as an independent predictor of infectious complications.
Humans ; Gastrointestinal Microbiome ; Leukemia, Myeloid, Acute/microbiology* ; Induction Chemotherapy ; Feces/microbiology* ; Male ; Female ; Middle Aged

BACKGROUND: Based on the China-VHD database, this study sought to develop and validate a Valvular Heart Disease- specific Age-adjusted Comorbidity Index (VHD-ACI) for predicting mortality risk in patients with VHD. METHODS & RESULTS: The China-VHD study was a nationwide, multi-centre multi-centre cohort study enrolling 13,917 patients with moderate or severe VHD across 46 medical centres in China between April-June 2018. After excluding cases with missing key variables, 11,459 patients were retained for final analysis. The primary endpoint was 2-year all-cause mortality, with 941 deaths (10.0%) observed during follow-up. The VHD-ACI was derived after identifying 13 independent mortality predictors: cardiomyopathy, myocardial infarction, chronic obstructive pulmonary disease, pulmonary artery hypertension, low body weight, anaemia, hypoalbuminaemia, renal insufficiency, moderate/severe hepatic dysfunction, heart failure, cancer, NYHA functional class and age. The index exhibited good discrimination (AUC, 0.79) and calibration (Brier score, 0.062) in the total cohort, outperforming both EuroSCORE II and ACCI (P < 0.001 for comparison). Internal validation through 100 bootstrap iterations yielded a C statistic of 0.694 (95% CI: 0.665-0.723) for 2-year mortality prediction. VHD-ACI scores, as a continuous variable (VHD-ACI score: adjusted HR (95% CI): 1.263 (1.245-1.282), P < 0.001) or categorized using thresholds determined by the Yoden index (VHD-ACI ≥ 9 vs. < 9, adjusted HR (95% CI): 6.216 (5.378-7.184), P < 0.001), were independently associated with mortality. The prognostic performance remained consistent across all VHD subtypes (aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid valve disease, mixed aortic/mitral valve disease and multiple VHD), and clinical subgroups stratified by therapeutic strategy, LVEF status (preserved vs. reduced), disease severity and etiology. CONCLUSION The VHD-ACI is a simple 13-comorbidity algorithm for the prediction of mortality in VHD patients and providing a simple and rapid tool for risk stratification.